准确刻画不同网络应用流量的行为特征,是识别和控制应用流量以及保证互联网服务质量的关键。该文提出一种基于流量系统状态递归特性的分析方法,研究应用流量的内在动力学行为。针对实际网络中多类不同应用流量的时间序列,首先重构流量序列的高维相空间,然后分析应用流量系统状态运动轨迹的递归特性,揭示其各自固有的内在行为。实验结果表明,流量的非线性动力学特征能够准确地刻画各类网络应用流量的行为,并且不随网络规模或网络协议版本的改变而发生变化。因此,流量的非线性动力学特征有助于提高互联网应用流量识别与控制的性能。

Accurate characterization of the traffic from different network applications plays an important role in traffic classifications to guarantee the quality of service of Internet traffic. The behavior of various network application traffic was analyzed based on the recurrence properties of the network traffic. A high-dimensional phase space is constructed for the traffic time series and then recurrences in the traffic state trajectory are analyzed to identify the intrinsic characteristics of the application traffic. Analyses show that the nonlinear dynamic features can accurately characterize application traffic behavior and that these features are independent of the network scale and Internet protocol version. Therefore, the nonlinear dynamics of application traffic can be used to improve network traffic classification.